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Quantocracy’s Daily Wrap for 02/24/2024

This is a summary of links featured on Quantocracy on Saturday, 02/24/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Building Intuition for Trading with Convex Optimisation with CVXR [Robot Wealth]

    This article continues our recent stat arb series. The previous articles are linked below: A short take on stat arb trading in the real world A general approach for exploiting stat arb alphas Ideas for crypto stat arb features Quantifying and combining crypto alphas A simple and effective way to manage turnover and not get killed by costs How to model features as expected returns Next, well
  • Build state-of-the-art portfolios with machine learning [PyQuant News]

    Portfolio optimization usually requires an estimate of the future returns of the assets in the portfolio. This is hard because we cant see into the future. Traditional risk parity uses a quadratic optimizer A cutting edge technique called Hierarchical Risk Parity (HRP) uses graph theory and machine learning to build a hierarchical structure of the investments. By the end of todays
  • Regression-based macro trading signals [SR SV]

    Regression is one method for combining macro indicators into a single trading signal. Specifically, statistical learning based on regression can optimize model parameters and hyperparameters sequentially and produce signals based on whatever model has predicted returns best up to a point in time. This method learns from growing datasets and produces valid point-in-time signals for backtesting.
  • Biotech stocks – is making a bet on them a lottery ticket? [Alpha Architect]

    The academic research, including the 2023 studies Lottery Preference and Anomalies and Do the Rich Gamble in the Stock Market? Low Risk Anomalies and Wealthy Households, the 2022 study Lottery Demand and the Asset Growth Anomaly, and the 2014 study Do Investors Overpay for Stocks with Lottery-like Payoffs? An Examination of the Returns on OTC Stocks, has found that there

Quantocracy’s Daily Wrap for 02/20/2024

This is a summary of links featured on Quantocracy on Tuesday, 02/20/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Robustness Testing of Country and Asset ETF Momentum Strategies [Quantpedia]

    The investment world witnessed a paradigm shift with the introduction of momentum strategies, a concept pioneered by Jagadeesh and Titman in their landmark 1993 study Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Their groundbreaking approach, hinged on the concept of buying stocks with a strong past performance (over 3- to 12-month periods) and
  • Python vs. Wolfram Language [Jonathan Kinlay]

    As an avid user of both Python and Wolfram Language for technical computing, Im often asked how they compare. Pythons strengths as an open-source language are clear: Ubiquity With millions of users, Python has become ubiquitous across fields like data science, ML engineering, web development, and scientific research. This massive adoption fuels continuous enhancement of its tools.
  • Absolute versus Relative Momentum Across Asset Classes [Finominal]

    Absolute and relative momentum can be used as simple asset allocation frameworks Both would have generated a higher return than an equal-weighted portfolio across asset classes However, risk-adjusted returns were lower and drawdowns higher INTRODUCTION Investing is often overwhelming given the enormous number of strategies and asset classes that are available to investors. Deciding on how to
  • Benchmark selection: addressing strategic distortions [Alpha Architect]

    The paper aims to provide insights into the dynamics of benchmark selection, the effectiveness of Relative Performance Evaluation ( RPE ) incentivization, and the broader implications for fund performance and market competition. Self-Declared Benchmarks and Fund Manager Intent: Cheating or Competing? Chen, Evans and Sun FMA working, 2024 A version of this paper can be found here Want to read

Quantocracy’s Daily Wrap for 02/18/2024

This is a summary of links featured on Quantocracy on Sunday, 02/18/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Model Features as Expected Returns [Robot Wealth]

    Modeling features as expected returns can be a useful way to develop trading strategies, but it requires some care. The main advantage is that it directly aligns with the objective of predicting and capitalising on future returns. This can make optimisation and implementation more intuitive. It also facilitates direct comparison between features and provides a common framework for incorporating

Quantocracy’s Daily Wrap for 02/17/2024

This is a summary of links featured on Quantocracy on Saturday, 02/17/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Gauging Existing Technical Fundamental Features through Mutual Information [Quantpedia]

    Investing truly is an intense intellectual undertaking. For a Portfolio Manager (PM) to execute an investment, they must first convince themselves, then others, that the rationale behind the investment is sound. The variables they utilize in developing their rationale are of the upmost importance; These variables inevitably serve as a foundation in the evaluation of a given Asset, and therefore
  • How to download more fundamental data to power trading [PyQuant News]

    Quants, financial analysis, and traders use fundamental data for investing and trading. These data are derived from quarterly and annual statements that companies file with the U.S. Securities Exchange Commission (SEC). These statements are rich with data that can be used to build predictive factor models for investment portfolios. The problem? We cant download all these documents, parse them,
  • On the Persistence of Growth and Value Stocks [Alpha Architect]

    Expectations of future earnings growth matter a great deal to valuations because investors, in their collective wisdom, assign higher valuations to companies they expect will grow more quickly in the future (growth stocks). In contrast, firms expected to show slower growth (value stocks) are assigned lower valuations. An implicit assumption in most forecasts is that growth is persistent. While

Quantocracy’s Daily Wrap for 02/14/2024

This is a summary of links featured on Quantocracy on Wednesday, 02/14/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Defensive Trend [Return Sources]

    Like the Federal Reserve, trend following is often said to have a dual mandate. One mandate is to earn a positive return, and the other is to provide some sort of crisis alpha, or an offset to drawdowns in traditional, 60/40 type portfolios. There could be tension between these two goals; for example, should trend followers take long positions in equity indices? This will likely improve

Quantocracy’s Daily Wrap for 02/12/2024

This is a summary of links featured on Quantocracy on Monday, 02/12/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • European Investors and TAA Strategies: Four Approaches [Allocate Smartly]

    We track 80+ Tactical Asset Allocation (TAA) strategies, most of which were designed from the perspective of a US investor trading US ETFs. Most European investors cant access US ETFs, instead trading UCITS funds listed on non-US exchanges, often denominated in currencies other than USD. In this post well provide data analyzing four approaches a European investor might take in trading US TAA
  • Prompting is Programming with LMQL [Gautier Marti]

    In this blog, I just toy around with a relatively new framework for querying (large) language models: LMQL, a SQL-like for LLMs. It is a first step toward a novel programming paradigm: Language Model Programming (LMP). These ideas are described in the very interesting paper Prompting Is Programming: A Query Language for Large Language Models. From time to time, Machine Learners revisit the concept
  • ChatGPT – can it be used to select investments? [Alpha Architect]

    One use of the NLP (natural language processing) features of ChatGPT is to search out patterns in the immense amounts of news, data and other sources of information about specific stocks, and then efficiently convert them into summaries valuable for all types of investors. Can this be accomplished with useful results? The authors use the Q2_2023 period to test performance around earnings
  • Duration of U.S. Equities – II [Finominal]

    There are multiple ways to measure interest rate sensitivities High-duration stocks like tech and biotech were not more sensitive to rising rates The relationship between interest rates and stocks is weak INTRODUCTION In our first article on the duration of U.S. equities (read Duration of U.S. Equities) we concluded that the interest rate sensitivity of the stock market ranged significantly

Quantocracy’s Daily Wrap for 02/11/2024

This is a summary of links featured on Quantocracy on Sunday, 02/11/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Simple, Effective Way to Manage Turnover and Not Get Killed by Costs [Robot Wealth]

    Every time we trade, we incur a cost. We pay a commission to the exchange or broker, we cross spreads, and we might even have market impact to contend with. A common issue in quant trading is to find an edge, only to discover that if you executed it naively, youd get killed with costs. In this article, Ill show you an example of using a simple heuristic that helps you do an optimal amount of
  • How to exploit the month-end flow effect for a 502% return [PyQuant News]

    Fund managers report their holdings every month. They dont want to tell investors that they lost money the latest meme stock. So they will sell the meme stocks and buy higher quality assets, like bonds. We might be able to take advantage of this month-end flow effect by buying bonds toward the end of the month and selling them at the beginning. The month-end flow effect is one of many
  • Generic derivative returns and carry (for strategy testing) [SR SV]

    Backtesting of macro trading strategies requires good approximate profit-and-loss data for standard derivatives positions, particularly in equity, foreign exchange, and rates markets. Practical calculation methods of generic proxy returns not only deliver valid strategy targets but are also the basis of volatility adjustments of trading factors and for calculating nominal and real carry of
  • Band of Brothers Attacking Short Sellers: Game Stop for Hedge Funds [Alpha Architect]

    In our book The Incredible Shrinking Alpha, Andrew Berkin and I presented the evidence demonstrating that the markets have become more efficient over time, making it more difficult to outperform the market on a risk-adjusted basis. Market efficiency explains the lack of persistent outperformance of actively managed funds beyond the randomly expected. Among the reasons we cited for the shrinking
  • Research Review | 9 February 2024 | Cross Market Analytics [Capital Spectator]

    A Changing Stock-Bond Correlation: Explaining Short-term Fluctuations Garth Flannery (BlueCove) and Daniel Bergstresser (Brandeis Intl Business School) December 2023 This paper builds on a framework that uses macroeconomic drivers to explain long-term variation in the correlation between stocks and bonds. The existing work focuses on the relative volatility of growth and inflation and the

Quantocracy’s Daily Wrap for 02/07/2024

This is a summary of links featured on Quantocracy on Wednesday, 02/07/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 02/05/2024

This is a summary of links featured on Quantocracy on Monday, 02/05/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Introducing max-GM, a new(?) performance statistic [Investment Idiocy]

    Do you remember this post? https://qoppac.blogspot.com/2022/06/vol-targeting-cagr-race.html Here I introduced a performance metric, the best annualised compounding return at the optimal leverage level for that strategy. This is equivalent to finding the highest geometric return once a strategy is run at it's Kelly optimal leverage. I've since played with that idea a bit, for example in
  • HY Bonds = High or Hazardous Yield? [Finominal]

    The correlation of high yield (HY) to investment-grade (IG) bonds has been increasing HY bonds can simply be replicated via a combination of the S&P 500 and IG bonds Replication portfolios offer better Sharpe ratios, which makes a case against using HY bonds in asset allocation INTRODUCTION When we recently ran a peer review analysis for BlackRocks 60/40 Target Allocation Fund (BIGPX) using

Quantocracy’s Daily Wrap for 02/04/2024

This is a summary of links featured on Quantocracy on Sunday, 02/04/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Replacing the 40, in R [Babbage9010]

    Elliot Rozner published a blog post recently proposing that one could replace the 40% bond portion of a 60/40 portfolio with a Long/Short equity trend following strategy. Here well put that idea (and his suggested approach) into R using quantmod and examine the components, and finally suggest a free-lunch tweak to improve the risk-adjusted returns further. Perhaps Im being a little flip
  • Overcoming experimenter bias in scientific research and finance [Mathematical Investor]

    Reproducibility has emerged as a major issue in numerous fields of scientific research, ranging from psychology, sociology, economics and finance to biomedicine, scientific computing and physics. Many of these difficulties arise from experimenter bias (also known as selection bias): consciously or unconsciously excluding, ignoring or adjusting certain data that do not seem to be in agreement